Scratch work
Code
| StateAbbr | StateDesc | CountyName | CountyFIPS | TractFIPS | TotalPopulation | ACCESS2_CrudePrev | ACCESS2_Crude95CI | ARTHRITIS_CrudePrev | ARTHRITIS_Crude95CI | ... | OBESITY_Crude95CI | PHLTH_CrudePrev | PHLTH_Crude95CI | SLEEP_CrudePrev | SLEEP_Crude95CI | STROKE_CrudePrev | STROKE_Crude95CI | TEETHLOST_CrudePrev | TEETHLOST_Crude95CI | Geolocation | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8871 | DC | District of Columbia | District of Columbia | 11001 | 11001007604 | 3644 | 9.0 | ( 7.0, 11.3) | 24.3 | (22.9, 25.7) | ... | (30.2, 34.0) | 10.4 | ( 9.2, 11.7) | 39.0 | (37.0, 40.8) | 4.8 | ( 4.2, 5.5) | 15.5 | ( 9.8, 22.1) | POINT (-76.96379997 38.86610065) |
| 8884 | DC | District of Columbia | District of Columbia | 11001 | 11001005600 | 6756 | 5.5 | ( 4.4, 7.0) | 13.7 | (13.0, 14.4) | ... | (15.6, 18.2) | 5.9 | ( 5.3, 6.7) | 26.2 | (24.3, 28.2) | 2.0 | ( 1.8, 2.2) | 7.6 | ( 5.1, 10.9) | POINT (-77.05385751 38.90021244) |
| 8919 | DC | District of Columbia | District of Columbia | 11001 | 11001008402 | 2149 | 5.2 | ( 4.1, 6.6) | 15.9 | (15.2, 16.8) | ... | (19.8, 22.0) | 5.7 | ( 5.1, 6.4) | 28.8 | (27.5, 30.3) | 2.4 | ( 2.1, 2.8) | 7.2 | ( 4.1, 14.4) | POINT (-76.9910702 38.89861357) |
| 9058 | DC | District of Columbia | District of Columbia | 11001 | 11001001100 | 4779 | 3.3 | ( 3.0, 3.8) | 18.8 | (18.1, 19.6) | ... | (16.0, 17.5) | 5.2 | ( 4.9, 5.7) | 23.6 | (22.8, 24.4) | 2.0 | ( 1.9, 2.2) | 3.4 | ( 2.6, 5.2) | POINT (-77.07767045 38.95722636) |
| 9075 | DC | District of Columbia | District of Columbia | 11001 | 11001008002 | 3031 | 4.7 | ( 3.8, 6.1) | 13.4 | (12.7, 14.2) | ... | (19.4, 21.8) | 4.9 | ( 4.4, 5.6) | 28.9 | (27.6, 30.5) | 1.8 | ( 1.6, 2.0) | 5.6 | ( 3.3, 9.7) | POINT (-76.98275018 38.89154628) |
5 rows × 67 columns
Code
# Resetting defaults and import plotly libraries
import plotly.io as pio
pio.renderers.default = "browser"
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook_connected"
# isolate only columns with CrudePrev in the name
dc_health_df_prev = dc_health_df.filter(regex='CrudePrev')
df = dc_health_df_prev
# Rename columns
df = df.rename(columns={'ACCESS2_CrudePrev': '% of Adults without Health Insurance',
'ARTHRITIS_CrudePrev': '% of Adults with Arthritis',
'BINGE_CrudePrev': '% of Adults who Binge Drink',
'BPHIGH_CrudePrev': '% of Adults with High Blood Pressure',
'BPMED_CrudePrev': '% of Adults with High Blood Pressure who take Blood Pressure Medication',
'CANCER_CrudePrev': '% of Adults who were Diagnosed with Cancer',
'CASTHMA_CrudePrev': '% of Adults who were Diagnosed with Asthma',
'CERVICAL_CrudePrev': '% of Women who had a Pap Smear in the Past 3 Years',
'CHD_CrudePrev': '% of Adults who were Diagnosed with Coronary Heart Disease',
'CHECKUP_CrudePrev': '% of Adults who had a Routine Checkup in the Past Year',
'CHOLSCREEN_CrudePrev': '% of Adults who had Cholesterol Checked in the Past 5 Years',
'COLON_SCREEN_CrudePrev': '% of Adults who had a Colonoscopy or similar test in the Past 10 Years',
'COPD_CrudePrev': '% of Adults who were Diagnosed with COPD (Chronic Obstructive Pulmonary Disease)',
'COREM_CrudePrev': '% Prevalence of Older Adult Men aged >=65 years who are up to date on preventative health',
'COREW_CrudePrev': '% Prevalence of Older Adult Women aged >=65 years who are up to date on preventative health',
'CSMOKING_CrudePrev': '% of Adults who Currently Smoke',
'DENTAL_CrudePrev': '% of Adults who had a Dental Visit in the Past Year',
'DEPRESSION_CrudePrev': '% of Adults who were Diagnosed with Depression',
'DIABETES_CrudePrev': '% of Adults who were Diagnosed with Diabetes',
'GHLTH_CrudePrev': '% of Adults who reported their Health as not Good',
'HIGHCHOL_CrudePrev': '% of Adults who were Diagnosed with High Cholesterol',
'KIDNEY_CrudePrev': '% of Adults who were Diagnosed with Kidney Disease',
'LPA_CrudePrev': '% of Adults who are Physically Inactive',
'MAMMOUSE_CrudePrev': '% Women aged 50-74 years who had a Mammogram in the Past 2 Years',
'MHLTH_CrudePrev': '% of Adults who reported their Mental Health as not Good',
'OBESITY_CrudePrev': '% of Adults who were Obese',
'PHLTH_CrudePrev': '% of Adults who reported their Physical Health as not Good',
'SLEEP_CrudePrev': '% of Adults who reported their Sleep as not Good',
'STROKE_CrudePrev': '% of Adults who were Diagnosed with Stroke',
'TEETHLOST_CrudePrev': '% of Adults who have lost all of their Natural Teeth'})
# list of health metrics for drop down menu
column_names = df.columns
# Creating the initial scatter plot
fig = go.Figure(go.Scatter(x=df[column_names[0]], y=df[column_names[1]], mode='markers'))
# Label axes
fig.update_xaxes(title_text='X Axis')
fig.update_yaxes(title_text='Y Axis')
# Setting the range for x and y axes
fig.update_xaxes(range=[0, 100])
fig.update_yaxes(range=[0, 100])
for col in column_names:
for col2 in column_names:
fig.add_trace(go.Scatter(x=df[col], y=df[col2], mode='markers', name=col + ' vs ' + col2, visible=False))
# Create a function to update the visibility of the traces based on selected columns
def update_visibility(selected_col, selected_col2):
for i, trace in enumerate(fig.data):
trace.visible = (trace.name == selected_col + ' vs ' + selected_col2)
# Create the drop-down menus for x (col) and y (col2) axes of the scatter plot
col_dropdown = [{'label': col, 'value': col} for col in column_names]
col2_dropdown = [{'label': col2, 'value': col2} for col2 in column_names]
# Define the dropdown menu for x-axis
button_layer_1_height = 1.08
x_axis_dropdown = go.layout.Updatemenu(
buttons=list([dict(args=[{'x': [df[col]]}, update_visibility(col, col2)], label=col, method='update') for col in column_names]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.06,
xanchor="left",
y=button_layer_1_height + 0.05,
yanchor="top"
)
# Define the dropdown menu for y-axis
y_axis_dropdown = go.layout.Updatemenu(
buttons=list([dict(args=[{'y': [df[col2]]}, update_visibility(col, col2)], label=col2, method='update') for col2 in column_names]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.06,
xanchor="left",
y=button_layer_1_height,
yanchor="top"
)
# Update the layout to include the dropdown menus
fig.update_layout(
updatemenus=[x_axis_dropdown, y_axis_dropdown]
)
# Label axes
fig.update_xaxes(title_text='X Axis')
fig.update_yaxes(title_text='Y Axis')
# Setting the range for x and y axes
fig.update_xaxes(range=[0, 100])
fig.update_yaxes(range=[0, 100])
# Update plot sizing
fig.update_layout(
width=900,
height=900,
autosize=False,
#margin=dict(t=100, b=0, l=0, r=0),
)
# add annotations
fig.update_layout(
annotations=[
dict(
text="X Axis:",
x=0,
xref="paper",
y=button_layer_1_height + 0.025,
yref="paper",
align="left",
showarrow=False
),
dict(
text="Y Axis:",
x=0,
xref="paper",
y=button_layer_1_height - 0.025,
yref="paper",
align="left",
showarrow=False
)
]
)
# Create a function to update the visibility of the traces based on selected columns
def update_visibility(selected_col, selected_col2):
for i, trace in enumerate(fig.data):
trace.visible = (trace.name == selected_col + ' vs ' + selected_col2)
# Display the scatter plot with dropdown menus
fig.show()
Code
| geoid_tract_20 | census_tract_name | depressed_perc | black_non_hisp_perc | white_non_hisp_perc | hispanic_latino_perc | 3_plus_cre_risk_factors_perc | below_poverty_level_perc | no_health_insurance_perc | male_perc | female_perc | income_inequality_gini_index | hs_grad_perc | households_no_vehicle_perc | households_w_internet_perc | walkability_score | GEOID | neighborhood_name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 11001009907 | Census Tract 99.07, District of Columbia, Dist... | 21.1 | 97.5 | 0.4 | 2.0 | 28.85 | 19.9 | 7.6 | 40.0 | 60.0 | 0.4280 | 88.6 | 54.3 | 64.4 | 12.250000 | 1.100101e+10 | MARSHALL HEIGHTS |
| 1 | 11001008802 | Census Tract 88.02, District of Columbia, Dist... | 18.3 | 68.7 | 25.6 | 2.7 | 35.38 | 19.7 | 4.3 | 51.1 | 48.9 | 0.4983 | 86.0 | 34.2 | 72.8 | 13.500000 | 1.100101e+10 | TRINIDAD |
| 2 | 11001005900 | Census Tract 59, District of Columbia, Distric... | 22.1 | 22.4 | 50.8 | 8.6 | 17.12 | 27.7 | 4.6 | 52.6 | 47.4 | 0.5047 | 90.7 | 61.1 | 82.6 | 16.500000 | 1.100101e+10 | CHINATOWN |
| 3 | 11001002900 | Census Tract 29, District of Columbia, Distric... | 19.3 | 19.6 | 47.1 | 25.0 | 13.98 | 8.2 | 7.8 | 50.1 | 49.9 | 0.4526 | 85.6 | 38.2 | 88.4 | 16.166667 | 1.100100e+10 | COLUMBIA HEIGHTS |
| 4 | 11001003302 | Census Tract 33.02, District of Columbia, Dist... | 18.5 | 37.6 | 48.7 | 5.2 | 9.09 | 9.3 | 1.4 | 46.3 | 53.7 | 0.3496 | 97.3 | 12.4 | 89.9 | 15.750000 | 1.100100e+10 | BLOOMINGDALE |
Code
Index(['StateAbbr', 'StateDesc', 'CountyName', 'CountyFIPS', 'census_tract',
'TotalPopulation', 'ACCESS2_CrudePrev', 'ACCESS2_Crude95CI',
'ARTHRITIS_CrudePrev', 'ARTHRITIS_Crude95CI', 'BINGE_CrudePrev',
'BINGE_Crude95CI', 'BPHIGH_CrudePrev', 'BPHIGH_Crude95CI',
'BPMED_CrudePrev', 'BPMED_Crude95CI', 'CANCER_CrudePrev',
'CANCER_Crude95CI', 'CASTHMA_CrudePrev', 'CASTHMA_Crude95CI',
'CERVICAL_CrudePrev', 'CERVICAL_Crude95CI', 'CHD_CrudePrev',
'CHD_Crude95CI', 'CHECKUP_CrudePrev', 'CHECKUP_Crude95CI',
'CHOLSCREEN_CrudePrev', 'CHOLSCREEN_Crude95CI',
'COLON_SCREEN_CrudePrev', 'COLON_SCREEN_Crude95CI', 'COPD_CrudePrev',
'COPD_Crude95CI', 'COREM_CrudePrev', 'COREM_Crude95CI',
'COREW_CrudePrev', 'COREW_Crude95CI', 'CSMOKING_CrudePrev',
'CSMOKING_Crude95CI', 'DENTAL_CrudePrev', 'DENTAL_Crude95CI',
'DEPRESSION_CrudePrev', 'DEPRESSION_Crude95CI', 'DIABETES_CrudePrev',
'DIABETES_Crude95CI', 'GHLTH_CrudePrev', 'GHLTH_Crude95CI',
'HIGHCHOL_CrudePrev', 'HIGHCHOL_Crude95CI', 'KIDNEY_CrudePrev',
'KIDNEY_Crude95CI', 'LPA_CrudePrev', 'LPA_Crude95CI',
'MAMMOUSE_CrudePrev', 'MAMMOUSE_Crude95CI', 'MHLTH_CrudePrev',
'MHLTH_Crude95CI', 'OBESITY_CrudePrev', 'OBESITY_Crude95CI',
'PHLTH_CrudePrev', 'PHLTH_Crude95CI', 'SLEEP_CrudePrev',
'SLEEP_Crude95CI', 'STROKE_CrudePrev', 'STROKE_Crude95CI',
'TEETHLOST_CrudePrev', 'TEETHLOST_Crude95CI', 'Geolocation',
'census_tract_name', 'depressed_perc', 'black_non_hisp_perc',
'white_non_hisp_perc', 'hispanic_latino_perc',
'3_plus_cre_risk_factors_perc', 'below_poverty_level_perc',
'no_health_insurance_perc', 'male_perc', 'female_perc',
'income_inequality_gini_index', 'hs_grad_perc',
'households_no_vehicle_perc', 'households_w_internet_perc',
'walkability_score', 'GEOID', 'neighborhood_name'],
dtype='object')Code
# Resetting defaults and import plotly libraries
import plotly.io as pio
pio.renderers.default = "browser"
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook_connected"
# isolate only columns with CrudePrev in the name
dc_health_df_prev = dc_health_df.filter(regex='CrudePrev')
df = dc_health_df_prev
# Rename columns
df = df.rename(columns={'ACCESS2_CrudePrev': '% of Adults without Health Insurance',
'ARTHRITIS_CrudePrev': '% of Adults with Arthritis',
'BINGE_CrudePrev': '% of Adults who Binge Drink',
'BPHIGH_CrudePrev': '% of Adults with High Blood Pressure',
'BPMED_CrudePrev': '% of Adults with High Blood Pressure who take Blood Pressure Medication',
'CANCER_CrudePrev': '% of Adults who were Diagnosed with Cancer',
'CASTHMA_CrudePrev': '% of Adults who were Diagnosed with Asthma',
'CERVICAL_CrudePrev': '% of Women who had a Pap Smear in the Past 3 Years',
'CHD_CrudePrev': '% of Adults who were Diagnosed with Coronary Heart Disease',
'CHECKUP_CrudePrev': '% of Adults who had a Routine Checkup in the Past Year',
'CHOLSCREEN_CrudePrev': '% of Adults who had Cholesterol Checked in the Past 5 Years',
'COLON_SCREEN_CrudePrev': '% of Adults who had a Colonoscopy or similar test in the Past 10 Years',
'COPD_CrudePrev': '% of Adults who were Diagnosed with COPD (Chronic Obstructive Pulmonary Disease)',
'COREM_CrudePrev': '% Prevalence of Older Adult Men aged >=65 years who are up to date on preventative health',
'COREW_CrudePrev': '% Prevalence of Older Adult Women aged >=65 years who are up to date on preventative health',
'CSMOKING_CrudePrev': '% of Adults who Currently Smoke',
'DENTAL_CrudePrev': '% of Adults who had a Dental Visit in the Past Year',
'DEPRESSION_CrudePrev': '% of Adults who were Diagnosed with Depression',
'DIABETES_CrudePrev': '% of Adults who were Diagnosed with Diabetes',
'GHLTH_CrudePrev': '% of Adults who reported their Health as not Good',
'HIGHCHOL_CrudePrev': '% of Adults who were Diagnosed with High Cholesterol',
'KIDNEY_CrudePrev': '% of Adults who were Diagnosed with Kidney Disease',
'LPA_CrudePrev': '% of Adults who are Physically Inactive',
'MAMMOUSE_CrudePrev': '% Women aged 50-74 years who had a Mammogram in the Past 2 Years',
'MHLTH_CrudePrev': '% of Adults who reported their Mental Health as not Good',
'OBESITY_CrudePrev': '% of Adults who were Obese',
'PHLTH_CrudePrev': '% of Adults who reported their Physical Health as not Good',
'SLEEP_CrudePrev': '% of Adults who reported their Sleep as not Good',
'STROKE_CrudePrev': '% of Adults who were Diagnosed with Stroke',
'TEETHLOST_CrudePrev': '% of Adults who have lost all of their Natural Teeth'})
# list of health metrics for drop down menu
column_names = df.columns
# Creating the initial scatter plot
fig = go.Figure(go.Scatter(x=df[column_names[0]], y=df[column_names[1]], mode='markers'))
# Label axes
fig.update_xaxes(title_text='X Axis')
fig.update_yaxes(title_text='Y Axis')
# Setting the range for x and y axes
fig.update_xaxes(range=[0, 100])
fig.update_yaxes(range=[0, 100])
for col in column_names:
for col2 in column_names:
fig.add_trace(go.Scatter(x=df[col], y=df[col2], mode='markers', name=col + ' vs ' + col2, visible=False))
# Create a function to update the visibility of the traces based on selected columns
def update_visibility(selected_col, selected_col2):
for i, trace in enumerate(fig.data):
trace.visible = (trace.name == selected_col + ' vs ' + selected_col2)
# Create the drop-down menus for x (col) and y (col2) axes of the scatter plot
col_dropdown = [{'label': col, 'value': col} for col in column_names]
col2_dropdown = [{'label': col2, 'value': col2} for col2 in column_names]
# Define the dropdown menu for x-axis
button_layer_1_height = 1.08
# Define the dropdown menu for y-axis
y_axis_dropdown = go.layout.Updatemenu(
buttons=list([dict(args=[{'y': [df[col2]]}, update_visibility(col, col2)], label=col2, method='update') for col2 in column_names]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.06,
xanchor="left",
y=button_layer_1_height,
yanchor="top"
)
# Update the layout to include the dropdown menus
fig.update_layout(
updatemenus=[y_axis_dropdown]
)
# Label axes
fig.update_xaxes(title_text='X Axis')
fig.update_yaxes(title_text='Y Axis')
# Setting the range for x and y axes
fig.update_xaxes(range=[0, 100])
fig.update_yaxes(range=[0, 100])
# Update plot sizing
fig.update_layout(
width=900,
height=900,
autosize=False,
#margin=dict(t=100, b=0, l=0, r=0),
)
# add annotations
fig.update_layout(
annotations=[
dict(
text="X Axis: Walkability",
x=0,
xref="paper",
y=button_layer_1_height + 0.025,
yref="paper",
align="left",
showarrow=False
),
dict(
text="Y Axis:",
x=0,
xref="paper",
y=button_layer_1_height - 0.025,
yref="paper",
align="left",
showarrow=False
)
]
)
# Create a function to update the visibility of the traces based on selected columns
def update_visibility(selected_col, selected_col2):
for i, trace in enumerate(fig.data):
trace.visible = (trace.name == selected_col + ' vs ' + selected_col2)
# Display the scatter plot with dropdown menus
fig.show()